The Generation Challenge programme (GCP) is a global crop research consortium directed toward crop improvement through the application of comparative biology and genetic resources characterization to plant breeding. A...
The Generation Challenge programme (GCP) is a global crop research consortium directed toward crop improvement through the application of comparative biology and genetic resources characterization to plant breeding. A key consortium research activity is the development of a GCP crop bioinformatics platform to support GCP research. This platform includes the following: (i) shared, public platform-independent domain models, ontology, and data formats to enable interoperability of data and analysis flows within the platform;(ii) web service and registry technologies to identify, share, and integrate information across diverse, globally dispersed data sources, as well as to access high-performance computational (HPC) facilities for computationally intensive, high-throughput analyses of project data;(iii) platform-specific middleware reference implementations of the domain model integrating a suite of public (largely open-access/-source) databases and software tools into a workbench to facilitate biodiversity analysis, comparative analysis of crop genomic data, and plant breeding decision making.
作者:
Kudo, Shin eiLambert, ReneAllen, John I.Fujii, HiroakiFujii, TakahiroKashida, HiroshiMatsuda, TakahisaMori, MasakiSaito, HiroshiShimoda, TadakazuTanaka, ShinjiWatanabe, HidenobuSung, Joseph J.Feld, Andrew D.Inadomi, John M.O'Brien, Michael J.Lieberman, David A.Ransohoff, David F.Soetikno, Roy M.Triadafilopoulos, GeorgeZauber, AnnTeixeira, Claudio RolimRey, Jean FranigoisJaramillo, EdgarRubio, Carlos A.Van Gossum, AndreJung, MichaelVieth, MichaelJass, Jeremy R.Hurlstone, Paul D.[a]Current affiliations: Hiroaki Fujii
MD Department of Pathology (II) Juntendo University School of Medicine Tokyo Japan. Takahiro Fujii MD TF Clinic Ginza Tokyo Japan. Hiroshi Kashida MD Shin ei Kudo MD Digestive Disease Center Northern Yokohama Hospital Showa University Yokohama Japan. Takahisa Matsuda MD Division of Endoscopy Tadakazu Shimoda MD Clinical Laboratory Division National Cancer Centre Hospital Tokyo Japan. Masaki Mori MD PhD FACS Department of Surgery Medical Institute of Bioregulation Kyushu University Hospital Beppu Japan. Hiroshi Saito MD Cancer Screening Technology Division Research Center for Cancer Prevention and Screening National Cancer Center Tokyo Japan. Shinji Tanaka MD Department of Endoscopy Hiroshima University Hospital Hiroshima Japan. Hidenobu Watanabe MD Division of Molecular and Diagnostic Pathology Graduate School of Medical and Dental Sciences Niigata University Niigata Japan. Joseph J. Sung MD Institute of Digestive Diseases Chinese University of Hong Kong Prince of Wales Hospital Shatin Hong Kong. John I. Allen MD Medical Director Minnesota Gastroenterology University of Minnesota School of Medicine Minneapolis Minnesota USA. Andrew D. Feld MD Clinical Associate Professor of Medicine Central Division of Gastroenterology Group Health Cooperative University of Washington Seattle Washington USA. John M. Inadomi MD Director GI Health Outcomes Research Program University of California San Francisco Division of Gastroenterology San Francisco General Hospital San Francisco California USA. Michael J. O'Brien MD Department of Pathology Boston University Medical Center Boston Massachusetts USA. David A. Lieberman MD Division of Gastroenterology Portland VA Hospital Portland Oregon USA. David F. Ransohoff MD Bioinformatics Department of Medicine University of North Carolina Chapel Hill North Carolina USA. Roy M. Soetikno MD Gastroenterology Section Veterans Affairs Palo Alto Health
The ability to identify protein binding sites and to detect specific amino acid residues that contribute to the specificity and affinity of protein interactions has important implications for problems ranging from rat...
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The ability to identify protein binding sites and to detect specific amino acid residues that contribute to the specificity and affinity of protein interactions has important implications for problems ranging from rational drug design to analysis of metabolic and signal transduction networks. Support vector machines (SVM) and related kernel methods offer an attractive approach to predicting protein binding sites. An appropriate choice of the kernel function is critical to the performance of SVM. Kernel functions offer a way to incorporate domain-specific knowledge into the classifier. We compare the performance of three types of kernels functions: identity kernel, sequence-alignment kernel, and amino acid substitution matrix kernel in the case of SVM classifiers for predicting protein-protein, protein-DNA and protein-RNA binding sites. The results show that the identity kernel is quite effective in on all three tasks. The substitution kernel based on amino acid substitution matrices that take into account structural or evolutionary conservation or physicochemical properties of amino acids yields modest improvement.
Protein-protein and protein nucleic acid interactions are vitally important for a wide range of biological processes, including regulation or gene expression, protein synthesis, and replication and assembly of many vi...
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ISBN:
(纸本)9812564632
Protein-protein and protein nucleic acid interactions are vitally important for a wide range of biological processes, including regulation or gene expression, protein synthesis, and replication and assembly of many viruses. We have developed machine learning approaches for predicting which amino acids of a protein participate in its interactions with other proteins and/or nucleic acids, using only the proiein sequence as input. In this paper, we describe an application of classifiers trained on datasets of well-characterized protein-protein and protein-RNA complexes for which experimental structures are available. We apply these classifiers to the problem of predicting protein and RNA binding sites in the sequence of a clinically important protein for which the structure is not known: the regulatory protein Rev, essential for the replication of HIV-I and other lentiviruses. We compare our predictions with published biochemical, genetic and partial structural information for HIV-1 and EIAV Rev and with our own published experimental mapping of RNA binding sites in EIAV Rev. The predicted and experimentally determined binding sites are in very good agreement. The ability to predict reliably the residues of a protein that directly contribute to specific binding events - without the requirement for structural information regarding either the protein or complexes in which it participates - can potentially generate new disease intervention strategies.
The experimental and computational techniques for capturing information about protein structures and genetic variation within the human genome have advanced dramatically in the past 20 years, generating extensive new ...
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The experimental and computational techniques for capturing information about protein structures and genetic variation within the human genome have advanced dramatically in the past 20 years, generating extensive new data resources. In this review, we discuss these advances, along with new approaches for determining the impact a genetic variant has on protein function. We focus on the potential of new methods that integrate human genetic variation into protein structures to discover relationships to disease, including the discovery of mutational hotspots in cancer-related proteins, the localization of protein-altering variants within protein regions for common complex diseases, and the assessment of variants of unknown significance for Mendelian traits. We expect that approaches that integratethese data sources will play increasingly important roles in disease gene discovery and variant interpretation.
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